Data lakes and data lakehouses provide a centralized repository for managing large data volumes. They serve as a foundation for collecting and analyzing structured, semi-structured and unstructured data in its native format for long-term storage and to drive insights and predictions. Unlike traditional data warehouses, they can process video, audio, logs, texts, social media, sensor data and documents to power apps, analytics and AI. They can also be built as part of a data fabric architecture to provide the right data, at the right time, regardless of where it is resides.

Fundamental data management challenges—silos, complexity and inconsistent data sets—limit your ability to use data to make employees and customers day-to-day workflows easier. Quality, actionable data for data scientists and business users requires a flexible data management approach that integrates with your existing technology with the deployment of your choice.

Practicing data science comes with challenges. It comes with fragmented data, a short supply of data science skills, and various tools, practices, and frameworks to choose from run with rigid IT standards for training and deployment. It's also challenging to operationalize ML models with unclear accuracy and difficult-to-audit predictions.
Using data science tools and solutions, you can accelerate AI-driven innovation with:
- An intelligent data fabric
- A simplified ModelOps lifecycle
- The ability to run any AI model with a flexible deployment
- Trusted and explainable AI
In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes. Moreover, you'll be able to manage and govern the AI lifecycle with ModelOps, optimize business decisions with prescriptive analytics and accelerate time to value with visual modeling tools.

To boost your company's decision-making abilities and support your business intelligence and AI initiatives, it is crucial to have a flexible and trusted data foundation. Our cloud native database and data warehouse technologies are specifically designed to store, manage and analyze all types of data and workloads, without the added complexities and security risks of data duplication or movement across environments. Integrations with data lakehouse make it easy for customers to share data in open formats and enable a single view of their analytics and AI estate.

Your data is everywhere, and that's part of the problem. Siloed data becomes an even bigger problem when your data and development teams also work in silos, causing a slow response to any incident. This lack of collaboration affects other areas of your business, too, from bug fixing to goal setting, making overall data use and operations inefficient.

With an OS DataOps platform, you can eliminate the distinction between data- and development-focused teams through collaborative work to develop an overview of the data acquisition journey. As a result, incident responses will accelerate, bugs will be fixed faster and a cohesive team will be able to set and update performance goals in real-time. Your data will be agile, accurate and efficient from end to end.